Deep Learning

About the course 

This is a comprehensive Deep Learning program designed to take learners from foundational concepts to advanced applications. The course covers preliminary machine learning concepts, deep learning architectures (ANN, CNN, RNN, LSTM, GRU, Transformers), optimization techniques, regularization, hyperparameter tuning, and specialized applications such as image processing, NLP, generative AI, time series forecasting, and recommender systems. Learners will also complete a capstone project and a final evaluation to consolidate their knowledge.

 

Course Objective:

The primary objectives of this course are:
• To provide a solid understanding of deep learning fundamentals, including data, model selection, and complexity.
• To explain the strengths and weaknesses of popular deep learning approaches.
• To build knowledge of the mathematical foundations underlying deep learning algorithms.
• To explore various learning paradigms (supervised, unsupervised, semi-supervised, reinforcement).
• To enable learners to design and implement deep learning models such as feed-forward networks, CNNs, RNNs, and Transformers.
• To apply deep learning techniques to real-world problems across domains like vision, language, forecasting, and recommendation.

 

Why enroll in this course?

Comprehensive Curriculum: Covers everything from basics to advanced architectures and applications.
Hands-On Learning: Includes practical implementation modules, quizzes, checkpoints, and a capstone project.
Industry-Relevant Skills: Prepares learners for careers in AI, data science, and machine learning by teaching cutting-edge techniques like transformers, generative AI, and recommender systems.
Balanced Approach: Focuses on both theoretical foundations and practical applications.
Final Evaluation: Ensures learners are job-ready with a structured assessment.

 

Who can enroll?

• Learners with a background in mathematics/statistics and computer programming skills.
• Those with prior knowledge of machine learning (recommended to complete a machine learning course first).
• Students, professionals, or researchers aiming to specialize in deep learning.
• Anyone interested in applying AI techniques to real-world applications such as image recognition, NLP, forecasting, or recommendation systems.

 

How will this course help you?

This course will help you:
• Build a strong foundation in deep learning concepts and architectures (ANN, CNN, RNN, Transformers).
• Gain practical skills through hands-on projects, quizzes, and a capstone.
• Learn to optimize, evaluate, and apply models to real-world domains like vision, NLP, forecasting, and recommendation.
• Be prepared for advanced research or industry roles in AI and data science.

  • Course Overview
  • Preliminary Concepts for Deep Learning
  • Introduction to Deep Learning
  • Calculating Model Parameters
  • Model Optimization
  • Regularization and Hyperparameter Tuning
  • Convolutional Neural Networks
  • Recurrent Neural Networks
  • Sequence-to-Sequence Architectures
  • Image Processing using Deep Learning
  • Natural Language Processing using Deep Learning
  • Generative Artificial Intelligence (Gen AI)
  • Time Series Forecasting using Deep Learning
  • Recommender Systems using Deep Learning

If you purchased a course directly through the platform as a retail user, a receipt for your order will be sent directly to the e-mail address you registered with.

If you received a token to enroll into a course through your organization/college, please contact your college for receipt details. No individual receipts are provided for such purchases

You will receive a token number (via e-mail for direct purchase on the platform or through your college/institutions IEEE-BLP program coordinator). Enter the token number on your dashboard on the IEEE BLP platform (after signing in at blended-learning.ieee.org ). You will be successfully enrolled to the course once the platform accepts the token as valid. Click on the Play button for the course on the dashboard to launch the e-Learning program or to perform other activities associated with the course. If you have trouble enrolling for courses, please send an e-mail to support-blp@ieee.org.

E-Learning access: Three months from the time you enter a valid token number on the platform or until you complete the course session, whichever comes later. The entire program (e-Learning course, Labs (if applicable), end-of-course assessment, and exam) must be completed within 6 months of purchase.

Please send us an e-mail at support-blp@ieee.org. You may be granted extension upon the sole discretion of the IEEE BLP Program administrator under exceptional circumstances.

Please refer to the course refund policy. You can write us at support-blp@ieee.org

Navigation Guides:If you have purchased a course from us, you would have received a ‘Getting started version of the Learner Navigation Guide’ from our support team. If you did not, please contact us at support-blp@ieee.org and we will be happy to share one. Help Lecture:If you are having trouble navigating the e-Learning, please click on the ‘Help Lecture’ (represented by a question mark symbol) present on every e-Learning slide.

Please double check to make sure that you have entered the correct token number. If you received the token number from your college/university administrator, please contact them to ensure that the same token number has not been shared with someone else previously. If you purchased the course directly on the platform, please send an e-mail request to support-blp@ieee.org for further help.

After purchasing the course you must activate it before using it. Your learner dashboard shows all the courses you are enrolled for. If you have enrolled for more than two courses, sign-in with your credentials and click on the arrow symbol to browse through all your courses.

The e-Learning part of the IEEE Blended Learning Program is specifically designed to be an interactive online course accessed through a web-browser interface. You would need access to a computer with appropriate configuration, an Internet connection, and appropriately configured audio. System requirements are provided in the program details page.

If you were in the middle of the assessment section, you will be asked to begin assessment again. Only the first, fully completed assessment results will be stored in the system for future retrieval.

When you resume e-Learning, you will have an option to begin from where you last left off. However, you will have to sign-in and re-launch the specific e-Learning course.

Please make sure that the audio on your computer is configured properly, has Internet connectivity, and other system requirements are met. If you are still facing an issue after checking your computers audio configuration, please send e-mail to support-blp@ieee.org.

You can control the audio playback in several ways. First, you can pause and resume the audio playback using audio controls available on the screen. You can also play audio from specific places within the page. Each page also includes a transcript of the audio to allow you to follow the audio more easily.

Please send e-mail describing, in detail, the issues you are facing along with your system details to support-blp@ieee.org. Please include the following information: Your system configuration (OS with version, RAM etc.) Browser and its version Nature of the issues including any screenshots

The Course Feedback link will be enabled only after 100% of both e-Learning and the lab sessions are completed. Please feel free to send specific feedback to feedback-blp@ieee.org

It is mandatory to complete the program 100% to get the certificate. (Not applicable for IEEE CertifAIEd, AI Ethics for System Design, and Responsible Procurement of AI.

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Original price was: ₹14,999.00.Current price is: ₹12,999.00.

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